CN108683897B - Intelligent correction method for distortion of multi-projection display system - Google Patents
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Abstract
The invention relates to an intelligent correction method for distortion of a multi-projection display system, which is characterized by comprising the following steps: connecting the camera with the computer through a cable, and connecting the projector with the computer through the cable; by designing a projection characteristic pattern with a central area as a unique identification criterion, after a camera collects the projected characteristic pattern, the position of the central area of the characteristic pattern is confirmed, and characteristic mapping solution is carried out on the periphery of the characteristic pattern by taking the central area as a reference, so that the problem that the mapping between a projector and a display surface cannot be solved when the projection display surface is lost in the projection characteristic pattern is solved.
Description
Technical Field
The invention relates to an intelligent correction method for distortion of a multi-projection display system, and belongs to the technical field of computer vision.
Background
With the rapid development of computer vision technology, projection exhibition display plays an increasingly important role in people's daily life, and the endless charm of multi-projection exhibition technology is fully shown in the world exposition in Shanghai, so that the multi-projection display system not only can enhance the resolution of a display picture, but also can provide an immersive scene. At present, the application range of a multi-projection system is wider and wider, projection interaction enhancing equipment is often found in various markets, and the interaction experience of users is very shocking, so that the multi-projection display technology becomes one of the current research hotspots. However, in order to implement seamless splicing of multiple projection display systems and implement high-resolution display pictures, a professional basically needs to adjust and control distortion of each projection display picture in a hardware manner, so that the flexibility of the multiple projection system is limited to a certain extent, and the corresponding labor cost is relatively high; meanwhile, in daily life, a multi-projection system is aimed at a conventional projection display surface, and if the multi-projection system is aimed at an unconventional projection display surface, serious distortion is faced. The increasingly complex deployment environment of multiple projection systems and non-conventional projection display surfaces therefore create new challenges for multiple projection correction techniques.
Distortion correction of a multi-projection system is usually performed by using a pre-designed feature pattern and a camera as auxiliary tools, that is, each projector unit is used to project the feature pattern, and the camera collects the feature pattern and solves the mapping relationship between each projector and a projection display surface, so as to perform distortion correction processing on a display picture corresponding to each projection unit. Therefore, the accuracy and timeliness of the geometric distortion correction of the multi-projection system depend on the resolution of the feature patterns of each projection unit design and the accuracy of the corresponding recognition algorithm. For example, AditiMajumder et al propose a method for solving a nonlinear mapping relationship between a projector unit and a projection display surface by using a Bezier curved surface to realize distortion correction of a projection display picture; ogata et al propose to use a matrix mapping method in combination with a polynomial correction method to achieve distortion correction. Although the method can realize distortion correction of a multi-projection display picture to a certain extent, problems such as solving errors of a nonlinear mapping relation and the like can be caused when projection characteristic patterns are projected by each projector unit and cannot be completely received by a projection display surface, so that the intelligent degree of the multi-projection display system is limited to a certain extent.
Disclosure of Invention
The invention aims to provide an intelligent correction method for distortion of a multi-projection display system, which avoids that a projection screen cannot completely display projection characteristic patterns due to the fact that the characteristic patterns projected by each projection unit are in the outer area of the projection display surface, so that a camera cannot correctly acquire the characteristic patterns of the projection display screen; a special projection characteristic pattern is designed, the center of a projection display area can be always displayed on the surface of a projection screen, so that the projection characteristic pattern is subjected to central area unique design processing, in the later characteristic identification, the problem of central area characteristic point matching is solved firstly, then matching characteristics are searched in four directions in parallel by taking the central area as a reference, and the nonlinear mapping relation between the characteristic pattern of the projection display area and the original characteristic pattern of a projector is solved.
The technical scheme of the invention is realized as follows: an intelligent correction method for distortion of a multi-projection display system is characterized in that: connecting the camera with the computer through a cable, and connecting the projector with the computer through the cable;
the method comprises the following specific steps:
Assigning values in the matrix pro _ marry to obtain the central position of each feature circle in the generated feature pattern;
step 4, converting the image campic _ i _ j collected by the camera by using the cvtColor function in opencv2.4.10 to obtain an image grappic _ i _ j, wherein i is 1,2 … hn, j is 1,2 … vn; carrying out binarization processing on the image graphic _ i _ j by using a threshold function in opencv2.4.10 to obtain an image binary _ i _ j, wherein i is 1,2 … hn, j is 1,2 … vn; then, carrying out edge detection on the image binary _ i _ j by using a function Canny in opencv2.4.10, and extracting a contour line of a feature circle in the image binary _ i _ j; and then, boundary positioning searching is carried out on the feature circle by using a function findContours in opencv2.4.10, and pixel point statistical analysis is carried out on the feature circle contour line which is detected and positioned, namely, a feature circumference threshold value cri _ thr is set, and the value can be set by calculating the average value of pixel points in all the feature circle contour lines which are detected and positioned. When the number of the pixel points in the contour line of the feature circle is larger than cri _ thr, the three great circles corresponding to the central area in the feature pattern are stored in the big _ curves set, otherwise, the three great circles are stored in the cir _ curves set, wherein the big _ curves set and the cir _ curves set are both in the type of opencv2.4.10 vector < vector > and the type of the cir _ curves set is not less than cri _ thr.
Step 5, constructing a matrix cam _ marry, wherein the size of the matrix cam _ marry is xnum rows and ynum columns, wherein the matrix value cam _ marry (i, j) of the ith row and the jth column in the matrix cam _ marry stores the cvPoint type in opencv2.4.10, namely, cam _ marry (i, j) ═ cx (i, j), cy (i, j)); using a fit Ellipse function in opencv2.4.10 to carry out feature map circle center positioning on the big _ constraints set to obtain three points 1(x1, y1), 2(x2, y2) and 3(x3, y3), and assigning the three points to corresponding cam _ marry matrixes; by using the correspondence of similar triangles, one-to-one correspondence between pro _ marry (5,5), pro _ marry (6,4), pro _ marry (6,7) and three points 1(x1, y1), point2(x2, y2), point3(x3, y3) is matched. And (3) positioning the center of a feature map of the cir _ constraints set by using a fitEllipse function in opencv2.4.10, storing the feature map into a cam _ marry (i, j) matrix, and matching pro _ marry matrixes corresponding to other elements in the cam _ marry matrix by using the matched three points as a reference.
And 6, establishing the corresponding relation between hn projection units in the horizontal direction and vn projection units in the vertical direction in the multi-projection system and the features in the feature circle pattern in the camera coordinate space through the steps 4 and 5.
Step 7, using the formula
Wherein, the index n of px (n, m) and cx (n, m) is xnum, y is ynum, and k is 1,2 … hn is multiplied by vn, the matched feature points in step 6 are brought into the formula, and the formula is solved by the least square method
Wherein T represents the transposition of the matrix, and the solution is carried out by the least square method, thus obtaining each mapping relation M in the multi-projection system by solutionkThe value of (c).
And 8: projecting original images corresponding to each projection unit in the multi-projection system by utilizing a nonlinear mapping relation MkAnd carrying out affine transformation to obtain a geometrically distorted pre-projected image corresponding to each projection unit.
And step 9: and projecting the corresponding geometric distortion pre-projection images to the projection display screen 4 by using the projection units so as to obtain multi-projection distortion-free images.
The method has the advantages that the intelligent correction of the distortion of the multi-projection display system can effectively avoid the problem that the characteristic patterns projected by each projection unit are in the outer area of the projection display surface, so that the projection screen cannot completely display the projection characteristic patterns, the camera cannot correctly acquire the characteristic patterns of the projection display screen, and the problems of excessive manual intervention and adjustment of the placement posture of the projector and the like can be avoided.
The invention has the advantages that the problem that the projection characteristic patterns cannot be completely presented on the projection display surface due to the arrangement postures of all the projection units to cause the failure of the nonlinear distortion correction of a multi-projection system is solved, and the problem that the mapping between a projector and the display surface cannot be solved due to the loss of the projection display surface in the projection characteristic patterns is solved by designing the projection characteristic patterns with the central area as the uniqueness recognition criterion, confirming the position of the central area of the characteristic patterns and carrying out characteristic mapping solution to the periphery with the central area as the reference after the camera collects the projection characteristic patterns.
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Fig. 1 is a schematic structural diagram of the present invention, wherein a computer 1, a camera 2, a projection unit 3, and a projection display screen 4 are provided.
Detailed Description
The invention is further described with reference to the accompanying drawings in which: as shown in fig. 1, an intelligent correction method for distortion of a multi-projection display system comprises a computer 1, a camera 2, a projection unit 3 and a projection display screen 4; the method is characterized in that: the camera 2 is connected with the computer 1 through a cable, and the projector 3 is connected with the computer 1 through a cable;
the method comprises the following specific steps:
And assigning values in the matrix pro _ marry to obtain the central position of each feature circle in the generated feature pattern.
And 3, for a multi-projection system with 2 projection units 3 in the horizontal direction hn and 1 projection unit 3 in the vertical direction vn, sequentially projecting the generated projection characteristic image pro _ pattern to a projection display screen 4 from left to right and from top to bottom, and simultaneously, sequentially acquiring images campic _ i _ j, i being 1,2 … hn, j being 1 and 2 … vn by keeping the pose of the camera 2 fixed.
Step 4, converting the image campic _ i _ j collected by the camera by using the cvtColor function in opencv2.4.10 to obtain an image grappic _ i _ j, wherein i is 1,2 … hn, j is 1,2 … vn; carrying out binarization processing on the image graphic _ i _ j by using a threshold function in opencv2.4.10 to obtain an image binary _ i _ j, wherein i is 1,2 … hn, j is 1,2 … vn; then, carrying out edge detection on the image binary _ i _ j by using a function Canny in opencv2.4.10, and extracting a contour line of a feature circle in the image binary _ i _ j; and then, boundary positioning searching is carried out on the feature circle by using a function findContours in opencv2.4.10, and pixel point statistical analysis is carried out on the feature circle contour line which is detected and positioned, namely, a feature circumference threshold value cri _ thr is set, and the value can be set by calculating the average value of pixel points in all the feature circle contour lines which are detected and positioned. When the number of the pixel points in the contour line of the feature circle is larger than cri _ thr, the three great circles corresponding to the central area in the feature pattern are stored in the big _ curves set, otherwise, the three great circles are stored in the cir _ curves set, wherein the big _ curves set and the cir _ curves set are both in the type of opencv2.4.10 vector < vector > and the type of the cir _ curves set is not less than cri _ thr.
Step 5, constructing a matrix cam _ marry, wherein the size of the matrix cam _ marry is xnum rows and ynum columns, wherein the matrix value cam _ marry (i, j) of the ith row and the jth column in the matrix cam _ marry stores the cvPoint type in opencv2.4.10, namely, cam _ marry (i, j) ═ cx (i, j), cy (i, j)); using a fit Ellipse function in opencv2.4.10 to carry out feature map circle center positioning on the big _ constraints set to obtain three points 1(x1, y1), 2(x2, y2) and 3(x3, y3), and assigning the three points to corresponding cam _ marry matrixes; by using the correspondence of similar triangles, one-to-one correspondence between pro _ marry (5,5), pro _ marry (6,4), pro _ marry (6,7) and three points 1(x1, y1), point2(x2, y2), point3(x3, y3) is matched. And (3) positioning the center of a feature map of the cir _ constraints set by using a fitEllipse function in opencv2.4.10, storing the feature map into a cam _ marry (i, j) matrix, and matching pro _ marry matrixes corresponding to other elements in the cam _ marry matrix by using the matched three points as a reference.
And 6, establishing the corresponding relation between hn projection units in the horizontal direction and vn projection units in the vertical direction in the multi-projection system and the features in the feature circle pattern in the camera coordinate space through the steps 4 and 5.
Step 7, using the formula
Wherein px (n, m)And the index n of cx (n, m) is xnum, y is ynum, and k is 1,2 … hn × vn, the matched feature points in step 6 are brought into the above formula, and the above formula is solved by the least square method
Wherein T represents the transposition of the matrix, and the solution is carried out by the least square method, thus obtaining each mapping relation M in the multi-projection system by solutionkThe value of (c).
And 8: projecting original images corresponding to each projection unit in the multi-projection system by utilizing a nonlinear mapping relation MkAnd carrying out affine transformation to obtain a geometrically distorted pre-projected image corresponding to each projection unit.
And step 9: and projecting the corresponding geometric distortion pre-projection images to the projection display screen 4 by using the projection units so as to obtain multi-projection distortion-free images.
The method has the advantages that the intelligent correction of the distortion of the multi-projection display system can effectively avoid the problem that the characteristic patterns projected by each projection unit are in the outer area of the projection display surface, so that the projection screen cannot completely display the projection characteristic patterns, the camera cannot correctly acquire the characteristic patterns of the projection display screen, and the problems of excessive manual intervention and adjustment of the placement posture of the projector and the like can be avoided.
Claims (1)
1. An intelligent correction method for distortion of a multi-projection display system is characterized in that: connecting the camera with the computer through a cable, and connecting the projector with the computer through the cable;
the method comprises the following specific steps:
step 1, setting the radius cr of a characteristic circle in a projection characteristic pattern to be 10 pixels, the number xnum of the characteristic circle in the horizontal direction to be 10, and the number ymun of the characteristic circle in the vertical direction to be 10; constructing a matrix pro _ marry with the size of xnum rows and ynum columns, wherein the ith row and the jth column in the matrix pro _ marry have matrix values pro _ marry (i, j) for storing cvPoint types in opencv2.4.10, namely pro _ marry (i, j) ═ px (i, j), py (i, j)); where px (i, j) is an x-axis coordinate axis to which the center position of the feature circle is directed, py (i, j) is a y-axis coordinate axis to which the center position of the feature circle is directed, and the values thereof are obtained by using the formula
px(i,j)＝cr+(768-2×cr)/(xnum-1)×j
py(i,j)＝cr+(1024-2×cr)/(ynum-1)×i
Assigning values in the matrix pro _ marry to obtain the central position of each feature circle in the generated feature pattern;
step 2, when i is not equal to 5, j is not equal to 5, or i is not equal to 6, j is not equal to 4, or i is not equal to 6, j is not equal to 7, drawing a characteristic circle by using a circle function in opencv2.4.10, wherein the radius parameter in the circle function is cr to 10 pixels, the center parameter of the characteristic circle is a value corresponding to pro _ marry (i, j), and the color of the characteristic circle is Scalar (255 ); when i is 5, j is 5, i is 6, j is 4, or i is 6, j is 7, a circle function in opencv2.4.10 is used for drawing a feature circle, wherein a radius parameter in the circle function is cr is 15pixel, a center parameter of the feature circle is a value corresponding to pro _ marry (i, j), and a color of the feature circle is scale (255 ); a projection characteristic image pro _ pattern with black background color and white characteristic circle can be generated, and the resolution of the image is 1024 × 768;
step 3, for a multi-projection system with hn projection units (3) in the horizontal direction and vn projection units (3) in the vertical direction, projecting the generated projection characteristic image pro _ pattern to a projection display screen (4) in sequence from left to right and from top to bottom, and simultaneously acquiring images campic _ i _ j, i being 1,2 … hn, j being 1,2 … vn in sequence by using a camera (2) with a fixed pose;
step 4, converting the image campic _ i _ j collected by the camera by using the cvtColor function in opencv2.4.10 to obtain an image grappic _ i _ j, wherein i is 1,2 … hn, j is 1,2 … vn; carrying out binarization processing on the image graphic _ i _ j by using a threshold function in opencv2.4.10 to obtain an image binary _ i _ j, wherein i is 1,2 … hn, j is 1,2 … vn; then, carrying out edge detection on the image binary _ i _ j by using a function Canny in opencv2.4.10, and extracting a contour line of a feature circle in the image binary _ i _ j; secondly, boundary positioning and searching are carried out on the feature circle by using a function findContours of opencv2.4.10, pixel point statistical analysis is carried out on the feature circle contour line which is detected and positioned, namely a feature circumference threshold value cri _ thr is set, and the value can be set by calculating the average value of pixel points in all the feature circle contour lines which are detected and positioned; when the number of pixel points in the contour line of the feature circle is larger than cri _ thr, the three great circles corresponding to the central area in the feature pattern are stored in a big _ curves set, otherwise, the three great circles are stored in a cir _ curves set, wherein the big _ curves set and the cir _ curves set are both in the type of opencv2.4.10 vector < vector >;
step 5, constructing a matrix cam _ marry, wherein the size of the matrix cam _ marry is xnum rows and ynum columns, wherein the matrix value cam _ marry (i, j) of the ith row and the jth column in the matrix cam _ marry stores the cvPoint type in opencv2.4.10, namely, cam _ marry (i, j) ═ cx (i, j), cy (i, j)); using a fit Ellipse function in opencv2.4.10 to locate the centers of the feature maps of the big _ constraints set to obtain three points 1(x1, y1), point2(x2, y2) and point3(x3, y3), using the principle of similar triangles to search the corresponding matching relations of three points pro _ marry (5,5), pro _ marry (6,4) and pro _ marry (6,7), and using the matching relations of the three points and point1(x1, y1), point2(x2, y2) and point3(x3, y3), and using the matching relations to assign the corresponding elements in the corresponding cam _ marry matrix to the three points 1(x1, y1), point2(x2, y2), point3(x3, y 3); positioning the circle center of the cir _ contours set by using a fitEllips function in opencv2.4.10, and storing the circle center of the positioned cir _ contours set to a corresponding position in a cam _ marry (i, j) matrix by taking three points assigned in the cam _ marry as a reference;
step 6, establishing the corresponding relation between hn projection units in the horizontal direction and vn projection units in the vertical direction in the multi-projection system and the features in the feature circular patterns in the camera coordinate space through the steps 4 and 5;
step 7, using the formula
Wherein, the index n of px (n, m) and cx (n, m) is xnum, y is ynum, and k is 1,2 … hn is multiplied by vn, the matched feature points in step 6 are brought into the formula, and the formula is solved by the least square method
Wherein T represents the transposition of the matrix, and the solution is carried out by the least square method, thus obtaining each mapping relation M in the multi-projection system by solutionkA value of (d);
and 8: carrying out affine transformation on projection original images corresponding to all projection units in a multi-projection system by utilizing a nonlinear mapping relation so as to obtain geometric distortion pre-projection images corresponding to all projection units;
and step 9: and projecting the corresponding geometrically distorted pre-projected images onto a projection display screen (4) by using each projection unit so as to obtain multi-projection distortion-free images.
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